Dual Set Prediction Networks Based Joint Extraction of Entity and Relation

Abstract

The entity and relation extraction task, which is the technical source of constructing and updating large-scale knowledge graph, aims to identify the relationship between entities from unstructured text. Among the existing joint extraction methods of entity and relation, parallel decoding of tuples efficiently generates tuples by set prediction. However, this method ignores the interaction between entity and relationship, and entity subject and object, resulting in the generation of invalid tuples. To address this problem, this paper proposes a joint extraction model of entity and relation based on dual set prediction networks. To enhance the interaction between relationships and entities, a dual set prediction network is used to decode the tuples in parallel, and the entity information and relationship types in the tuples are generated sequentially. The first set prediction network models the set of tuples and decodes the subject-object information in the tuple. The second set prediction network models the set of tuples embedded with subject-object information and decodes the relationship type between subject and object. This paper designs an entity filter for entity subject-object, which predicts the subject-object correlation among entities in a sentence and filters out the tuples with lower subject-object correlation according to the result. Experiments on the NYT (New York Times) and WebNLG public datasets show that the proposed model performs better than the baseline model in terms of accuracy and F1 metrics when the encoder is BERT, which verifies the validity of the model.

Keywords

• joint extraction of entity and relation; dual set prediction networks; entities filter; parallel decoding